Against the dual carbon strategy and sustainable development goals, green technologies balancing high efficiency with environmental compatibility are urgently needed for environmental remediation. Significant advantages in remediating petroleum hydrocarbon (PHC) and heavy metal co-contamination are demonstrated by alkyl polyglucoside (APG), a bio-based nonionic surfactant. These advantages stem from its renewable feedstocks, efficient solubilization capacity, and rapid biodegradability. Knowledge graphs were generated through keyword visualization and co-occurrence analysis, revealing the temporal-spatial distribution, knowledge structure, and research hotspots and trends in this field, thus offering a new perspective for understanding the dynamics of APG-related research. Applications of APG in soil pollution remediation, as well as in industrial and agricultural sectors, are reviewed herein. The overall application and development path of APG indicates that it is in a stage of technological growth in environmental remediation and green surfactant development. Furthermore, APG frequently forms compound systems and synergistically couples with redox technologies, microbial systems, and plant systems to substantially enhance remediation efficacy. APG boasts prominent green advantages: its raw materials are derived from glucose and fatty alcohols, and its low-carbon synthesis process aligns with global carbon emission reduction demands. Looking forward, it is proposed that future efforts should focus on modifying the molecular structure of APG to improve targeted remediation capabilities, developing low-carbon and efficient preparation processes, and advancing multi-dimensional integration with nanomaterials and biotechnology. These measures will accelerate its large-scale application in green remediation, thereby providing sustainable solutions for technological innovation in environmental governance under the "dual-carbon" context.

Abstract Long‐term in situ aerosol observations are scarce on the Tibetan Plateau (TP) due to its harsh natural environment, limiting our ability to evaluate its climate effect at this critical region. This study presents the first year‐long real‐time observations of non‐refractory fine particulate matter (NR‐PM2.5) conducted in the northeastern TP, to explore the seasonal variation in aerosol characteristics. The average NR‐PM2.5 concentration is 4.7 ± 5.3 μg m−3 with a dominant contribution of organics. Our findings reveal distinct seasonal variations in NR‐PM2.5 mass loading and chemical composition, regulated by the alternation of the Asian summer monsoon and westerlies there, as well as the balance between the wet scavenging and dry deposition in each season. Specifically, enhanced aqueous‐phase chemical process during summer was observed contributing to stronger oxidation processes of organic aerosol with different oxidation pathways. These findings provided an in‐depth insight into the linkage between climate systems and atmospheric aerosols in the TP.

The China–Pakistan Economic Corridor (CPEC) has experienced increasing rainstorm flood risk, posing significant threats to infrastructure and socio-economic development under global environmental change. Evaluating flood risk along the corridor is crucial for informing disaster prevention, mitigation, and adaptive planning. This study develops a comprehensive flood risk assessment model based on the Hazard, Sensitivity, Vulnerability, and Coping capacity (HSVC) framework, integrating meteorological, geospatial, and socio-economic data from 1979 to 2024. The AHP–Entropy method was applied to determine the relative importance of factors, including extreme rainfall, natural conditions, population, and infrastructure. High-risk areas were identified primarily in the central plains and southern lowlands, forming continuous bands, whereas low- and mid-low-risk zones were concentrated in northern mountainous and transitional regions. Comparing 1979–1999 (P1) with 2000–2024 (P2), low- and mid-low-risk areas remained largely stable, while middle- to high-risk zones expanded significantly, indicating a gradual increase in overall flood risk. Extreme rainfall emerged as the dominant driver, exerting the strongest influence on flood intensity and spatial distribution, while sensitivity, vulnerability, and coping capacity further shaped the heterogeneous risk pattern. Model simulations showed high agreement with historical flood records, validating the approach. These findings provide a scientific basis for targeted flood management, infrastructure reinforcement, and resilience enhancement along the CPEC corridor.

Abstract Valley‐basin terrains represent one of Earth's most prominent landforms and host numerous urban settlements. However, these topographically constrained regions frequently experience severe winter aerosol pollution. One critical challenge in elucidating the formation and evolution mechanisms of valley aerosol pollution lies in the precise quantification of its complex vertical difference in aerosol chemistry. To address this, we conducted simultaneous high‐resolution real‐time field measurements at two distinct elevations (urban surface and mountaintop sites with approximately 640 m vertical separation) in Lanzhou, a typical urban valley in northwest China, in January 2021. Significant vertical differences were observed in submicron aerosol (PM1) chemical composition, sources, and temporal variations within this confined terrain. Primary emissions from residential cooking, traffic, and heating activities were major contributors to ground‐level PM1 (averaging 42%), whereas secondary aerosols dominated (76%) at the mountaintop. Most notably, vertical differences in primary aerosol contributions reached ∼40% during persistent cold‐air pool (CAP) episodes characterized by strong temperature inversions and suppressed development of boundary layers. Our study quantitatively reveals the vertical variations in aerosol chemistry, demonstrating that synoptic systems and boundary layer dynamics critically govern air quality in valley cities by regulating vertical mixing. Furthermore, these findings highlight that combining precise CAP weather forecasts with targeted primary emission controls could be a highly effective strategy for mitigating winter aerosol pollution in similar topographically confined regions globally.

ABSTRACT Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.

Self-organization of individual organisms at a very small scale may result in recognizable functional ecosystem structures at a larger spatial scale. Drylands, which cover almost half of emerged lands, host some of the most remarkable vegetation patterns on Earth, including "disordered hyperuniformity," a recently defined class of such emergent self-organization structures. Yet, the extent, causes, and consequences of disordered hyperuniform vegetation patterns in drylands remain virtually unknown. Here, we analyzed high-resolution remote sensing images of 425 spot-like drylands across the globe and found that disordered hyperuniformity shapes vegetation patterns in about one out of ten drylands, with the distribution of plants appearing to be "disordered" to the naked eye, but supporting highly recognizable (uniform) patterns at larger scales (ca. 50 to 500 m). Using mathematical models, we identify three potential mechanisms that can generate disordered hyperuniform vegetation patterns. These mechanisms are not limited to the well-studied Turing patterns and represent key general processes with respect to plant-plant or plant-sediment interactions. Further modeling indicates that disordered hyperuniformity enhances ecosystem functioning in terms of water retention use, and expands the range of aridity conditions under which the system can maintain itself, but may slow recovery of vegetation structure from disturbances. In a wider context, we also show that disordered hyperuniformity is likely to pertain to diverse dryland systems, such as termite-mound or fairy-circle landscapes. Our findings highlight that exploring disordered hyperuniformity of vegetation pattern of drylands (and potentially other large-scale systems) offers insights into the organization and resilience of ecosystems globally.

BackgroundEffective hydrological management in arid ecosystems requires a comprehensive understanding of how water flow conditions influence biota and ecosystem processes. However, the responses of soil microbial communities remain poorly understood. This study aims to evaluate whether, and to what extent, a 20-year difference in flow regimes affects soil microbial diversity, composition, and function. The composition of the soil microbial community and the factors shaping it were investigated in two arid riparian zones (East River and West River) of the lower Heihe River Basin using 16 S rRNA amplicon high-throughput sequencing.ResultsThe dominant phyla of the bacterial, fungal, and archaeal communities were Proteobacteria, Ascomycota, and Euryarchaeota, respectively. Co-occurrence network analysis revealed greater network connectivity and stability in the West River, suggesting enhanced mutualistic interactions and physiological acclimation strategies in response to low-flow conditions. Microbial-soil correlations varied with flow condition: bacterial and fungal communities under high-flow conditions were associated with bulk density and available nitrogen, whereas low-flow communities were more influenced by available potassium. High-flow conditions also strengthened the correlation between microbial communities and nitrogen-related functions. Functional prediction showed that chemoheterotrophy was the dominant bacterial function, and bacterial and archaeal communities were partially involved in the nitrogen cycle.ConclusionsChanges in flow regimes slightly modulated microbial community composition and diversity. However, microbial functions appeared to respond more strongly to hydrological factors than microbial diversity. Thus, alterations in microbial structure and function were jointly influenced by hydrological conditions and the availability of nutrients in arid riparian zones.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12866-025-04393-7.

Abstract. Aerosol particles transported from South Asia, especially biomass burning (BB) emission related aerosols during pre-monsoon, have significant climate effect in the Himalayas. The details on complicated physicochemical properties and aging process of aerosols are important for understanding this climate effect. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer co-located with gas analyzers was deployed during 25 April 2022 to 25 May 2022 to study the highly time-resolved chemical characteristics and aging process of submicron aerosols (PM1) on the northern slope of the Himalayas. The 10-min resolution mass concentration of PM1 varied from 0.1 to 12.2 µg m−3 during this study, with an average of 1.7 ± 1.6 µg m−3. Organic aerosols (OA) showed a dominant contribution (46.2 %) to PM1 following by sulfate (20.8 %), BC (19.4 %), ammonium (8.5 %), nitrate (4.8 %) and chloride (0.4 %). Evolution of bulk OA in the f44 vs. f60 space showed clear aging process from less aged BB plumes to highly oxidized state in polluted period. Positive matrix factorization (PMF) on the high-resolution organic mass spectra resolved two oxygenated OA (OOA) factors, i.e., a less-oxidized OOA influenced by biomass burning (OOA-BB) and a more-oxidized OOA (MO-OOA). We performed a case study to explore the OOA formation mechanism during long-range transport. The results indicated aqueous‐phase process and photochemical reaction together elevated OOA concentrations and ageing processing, consistent with secondary inorganic aerosol production. This study underscores the significant occurrence of BB aerosols in Himalayas and provides insights into the oxidative processing in this remote region.

Gully erosion, driven by the interplay of natural processes and human activities, results in severe soil degradation and landscape alteration, yet approaches for accurately quantifying erosion triggered by extreme precipitation using multi-source high-resolution remote sensing remain limited. This study first extracted digital surface models (DSM) for the years 2014 and 2024 using Ziyuan-3 and GaoFen-7 satellite stereo imagery, respectively. Subsequently, the DSM was calibrated using high-resolution unmanned aerial vehicle photogrammetry data to enhance elevation accuracy. Based on the corrected DSMs, gully erosion depths from 2014 to 2024 were quantified. Erosion patches were identified through a deep learning framework applied to GaoFen-1 and GaoFen-2 imagery. The analysis further explored the influences of natural processes and anthropogenic activities on elevation changes within the gully erosion watershed. Topographic monitoring in the Sandu River watershed revealed a net elevation loss of 2.6 m over 2014–2024, with erosion depths up to 8 m in some sub-watersheds. Elevation changes are primarily driven by extreme precipitation-induced erosion alongside human activities, resulting in substantial spatial variability in surface lowering across the watershed. This approach provides a refined assessment of the spatial and temporal evolution of gully erosion, offering valuable insights for soil conservation and sustainable land management strategies in the Loess Plateau region.

Desert grassland ecosystems on China’s Loess Plateau are characterized by diverse land use types and varying human disturbances. We aimed to evaluate how land use influences soil microbial communities and functional genes related to carbon (C) and phosphorus (P) cycling. To do this, we selected five representative land use types: natural grassland, 20-year abandoned farmland, 12-year alfalfa grassland, 5-year Lanzhou lily farmland, and 17-year Platycladus orientalis forest. High-throughput metagenomic sequencing and soil physicochemical analyses were conducted. Proteobacteria dominated the nutrient-rich lily soil, while Actinobacteria were more abundant in the other soils. Available phosphorus (AP) had the strongest influence on microbial community structure and gene composition (p < 0.01). The relative abundance of ppdK, rpiB, glpX, and epi (C fixation genes), and purS (purine metabolism) was significantly higher in forest soil than in abandoned farmland (p < 0.05). Similarly, forest soil showed elevated levels of mttB and acs (methanogenesis), sdhA (TCA cycle), pstS (P transport), and pps (pyruvate metabolism) compared to alfalfa soil. Lily soil exhibited significantly higher abundance of acr genes (involved in the hydroxypropionate–hydroxybutylate cycle) and phnE (an ATP-binding cassette transporter) than natural grassland and alfalfa soils (p < 0.05). Microbial networks involved in C and P cycling were simpler but more functionally specialized in forest soil. Positive microbial interactions related to C and P cycling were strongest in lily soil. These findings provide important insights into soil microbial functional adaptation and offer a foundation for sustainable land use management on the Loess Plateau.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12866-025-04305-9.